Analysis of multiple emotions from EEG signal using machine learning models

Emotion recognition is a valuable technique to monitor the emotional well-being of human being. It is found that around 60% of people suffer from different psychological conditions like depression, anxiety and other mental issues. Mental health studies explore how different emotional expressions are linked to specific psychological condition. Recognizing these patterns and identifying their emotions is complex in human being since it varies from each individual. Emotion represents the state of mind in response to the particular situation. These emotions that are collected using EEG electrode needs a fine grain emotional analysis to contribute for clinical analysis and personalized health monitoring. Most of the research works are based on valence and arousal (VA) resulting in two, three and four emotional classes based on their combinations. The main objective of this paper is to include dominance along with valence and arousal (VAD) resulting in the classification of 16 classes of emotional states and thereby improve the number of emotions to be identified. This paper also considers 2-class emotion, 4-class emotion and 16-class emotion classification problem and applies different models and discusses the evaluation methodology in order to select the best one. Among the six machine learning models, KNN proved to be the best model with the classification accuracy of 95.8% for 2- class, 91.78% for 4-class and 89.26% for 16-class . Performance metrics like Precision, ROC, Recall, F1-Score and Accuracy are evaluated. Additionally, statistical analysis has been performed using Friedmanchisquare test to validate the results. With the help of the experimentation, a suitable machine learning model that could perform for various classes has been identified.